It is well known in the Internet industry that some of the primary benchmarks by which online websites are measured include: a) ability to attract new visitors; b) visitor time/interaction on site or with the content; and c) user (subscriber) retention rate. With respect to the first benchmark, the current emphasis in research is on developing computing systems that are more effective at attracting visitors through enhanced user targeting, optimized content, customized engagement, etc. Conventionally this is done through targeted advertising to customized audiences (i.e., particular demographics, particular interests) which attempts to engage users and deliver eyeballs to the site/content in question. In other words, an outdoor activities magazine publisher may try to attract subscribers by delivering ads to social network members of a particular gender, age, location who have identified “camping” as an interest.
But current targeted advertising algorithms have limited effectiveness, even when those algorithms have access to a user's social/interest graphs. In other words, the measured click through rate (CTR) of current techniques is usually small, and/or the ratio of impressions to actions is small. As research has revealed, the CTR can be as low as 0.01% for most ads. Some social network sites have improved this rate by placing advertising content directly in user newsfeeds, instead of in typical side banner locations. In today's market, it is desirable to achieve significantly higher rates (on the order of 4-5%). The cost to achieve these rates may be high, because cost is based on charging per click (CPC) (or some other engagement) or per impression (CPM). A recent report by SalesForce.com, Inc., (SalesForce.com, Inc., “The Facebook Ads Benchmark Report,” Internet. Available at https://www(dot)salesforcemarketingcloud(dot)com/wp-content/uploads/2013/06/The-Facebook-Ads-Benchmark-Report.pdf. June, 2013.), the contents of which are incorporated by reference in their entirety, discusses these factors.
A notable result of this survey is the fact that computing systems configured to deliver certain types of ads (Sponsored Page Post Like Story) achieve more than 100 times better engagement rates than systems that only present conventional external website ads. These more effective types of ads incorporate content directly about a specific social network user and a specific merchant and are broadcast to members of the user's social graph.
Thus for a social networking site, or content publishing site, acquiring new users/customers solely through current advertising computing techniques is unpredictable, inefficient and expensive.
The second metric that conventional computing systems are measured against is their ability to engage and maintain user interest after users arrive at the Internet property/document. This benchmark is typically measured by monthly/daily active users based on time spent on site/session, actions taken, and so on. For example, Twitter (one social networking site) measures the number of timeline views for each user. In the case of Facebook, another social networking site, an active user is defined as a person who took an action to share content or activity with another friend. Other engagement metrics include statistics such as bounce rate; pages or content views per visit; number of shares of content per visit; average visit time on site, and so on. When a computing system is able to engage and retain a user's attention longer, the result is site bonding, opportunities for presenting advertising, new content, etc. Again, with all things being equal, systems that increase these figures relative to other systems are demonstrably more useful and desirable.
Finally, another metric is user retention rate, meaning, if a user is a member or subscriber, how well does the site's engagement logic retain such members? One standard benchmark measures such basic information as the number of members at the beginning of a period, the number of members at the end, and the number of new members. From these pieces of information, an online entity can compute basic benchmarks like retention rate, churn, etc. A recent article by Seufert (Seufert, B. “Minimum Viable Metrics for Mobile,” Internet. Available at http://mobiledevmemo(dot)com/minimum-viable-metrics/, Feb. 5, 2013), the contents of which are incorporated by reference in their entirety, provides a good summary of these metrics.
The Seufert article makes mention of another parameter, too, called “virality,” which is a key metric for mobile applications (“apps”). Generally speaking, one wants users to spread the adoption of apps through sharing, and this is measured by computing the average number of additional users each user introduces to the app. Achieving good virality figures is important because it substantially reduces the cost of advertising and overall user acquisition costs. As above, one focus of current research is on developing new computing systems which improve these figures compared to their predecessors.
It would be desirable, therefore, to develop new computing systems which improve the above metrics. Industries which have particular needs for improving their computing systems to perform better under these benchmarks include social networks and content publishing sites. These entities require continuous member growth and long retention to maintain profitability and sustainability. The established news/publishing industry in particular (e.g., The New York Times) is under severe pressure to monetize their content to readers and, to date, has been unable to compete against new era content providers using conventional technologies. Similarly, social network sites suffer from member attrition, because user engagement across social graphs is not targeted or attentive to user retention.
Prior art techniques solicit suggestions from friends for content. An app known as “Side” for example allows members to answer questions about other members. The questions are not directed to social graph activities or specific predictions per se, but rather open ended potential outside lifestyle activities. An example of a system that predicts user responses is described by Raza et al. in U.S. Patent Application Publication No. 2013/0103692, which is incorporated by reference herein. These existing apps require too much time and investment on the part of the user. In particular, the user is required to define a question that he/she is interested in, as opposed to presenting a predefined question on a single entry (rating) on something a user has already done (read an article). In addition while these apps are useful, they lack any substantial fun and game aspects, feedback, etc. which limits their use.